Interpretive Summary: Ways to improve the efficiency of plant breeding programs are often guided by quantitative genetics theory. This body of theory has often, but not always, been successful at predicting outcomes of selection methods. The theory, however, is based on several assumptions that appear to have little basis in biological reality. Ways to incorporate aspects of known biological complexity ito the theory without making the mathematics too complex are discussed.

Technical Abstract:
Ways to improve the efficiency of plant breeding programs are often guided by quantitative genetics theory. This body of theory has often, but not always, been successful at predicting relative outcomes of different selection methods. The theory, however, is based on several assumptions that appear to have little basis in biological reality. A more robust theory can be achieved by incorporating additional statistical parameters and by incorporating aspects of known biological mechanisms into the models. When this is done, however, the mathematical complexity of the theory increases to the point where it may be neither tractable nor informative. One possible way forward out of this situation is to use computer modeling and simulation of breeding programs in lieu of deterministic models.